# ipfml.utils

Utils functions of ipfml package (array normalization)

Functions

`get_entropy`(arr) | Returns the computed entropy from arr
`get_entropy_contribution_of_i`(arr, i) | Returns the entropy contribution i column
`get_entropy_without_i`(arr, i) | Returns the computed entropy from arr without contribution of i
`get_indices_of_highest_values`(arr, n) | Returns indices of n highest values from list or 1D numpy array
`get_indices_of_lowest_values`(arr, n) | Returns indices of n highest values from list or 1D numpy array
`integral_area_simps`(y_values, dx) | Returns area under curves from provided data points using Simpsons rule
`integral_area_trapz`(y_values, dx) | Returns area under curves from provided data points using Trapezium rule
`normalize_2D_arr`(arr) | Return array normalize from its min and max values
`normalize_arr`(arr) | Normalize data of 1D array shape
`normalize_arr_with_range`(arr, min, max) | Normalize data of 1D array shape

`ipfml.utils.``get_entropy`(arr)

  Returns the computed entropy from arr

  Parameters: | arr – numpy array
  Returns: | entropy score computed

  Example:

      >>> from ipfml import utils
      >>> import numpy as np
      >>> arr = np.arange(10)
      >>> entropy = utils.get_entropy(arr)
      >>> int(entropy)
      0

`ipfml.utils.``get_entropy_contribution_of_i`(arr, i)

  Returns the entropy contribution i column

  Parameters: | arr – numpy array i – column index
  Returns: | entropy contribution score computed

  Example:

      >>> from ipfml import utils
      >>> import numpy as np
      >>> arr = np.arange(10)
      >>> entropy = utils.get_entropy_contribution_of_i(arr, 3)
      >>> int(entropy)
      0

`ipfml.utils.``get_entropy_without_i`(arr, i)

  Returns the computed entropy from arr without contribution of i

  Parameters: | arr – numpy array i – column index
  Returns: | entropy score computed

  Example:

      >>> from ipfml import utils
      >>> import numpy as np
      >>> arr = np.arange(10)
      >>> entropy = utils.get_entropy_without_i(arr, 3)
      >>> int(entropy)
      0

`ipfml.utils.``get_indices_of_highest_values`(arr, n)

  Returns indices of n highest values from list or 1D numpy array

  Parameters: | arr – List of numpy array n – number of highest elements wanted
  Returns: | n indices of highest values

  Example:

      >>> from ipfml import utils
      >>> import numpy as np
      >>> arr = np.arange(10)
      >>> indices = utils.get_indices_of_highest_values(arr, 2)
      >>> indices
      array([9, 8])

`ipfml.utils.``get_indices_of_lowest_values`(arr, n)

  Returns indices of n highest values from list or 1D numpy array

  Parameters: | arr – List of numpy array n – number of highest elements wanted
  Returns: | n indices of highest values

  Example:

      >>> from ipfml import utils
      >>> import numpy as np
      >>> arr = np.arange(10)
      >>> indices = utils.get_indices_of_lowest_values(arr, 2)
      >>> indices
      array([0, 1])

`ipfml.utils.``integral_area_simps`(y_values, dx)

  Returns area under curves from provided data points using Simpsons rule

  Parameters: | y_values – y values of curve dx – number of unit for x axis
  Returns: | Area under curves obtained from these points

  Example:

      >>> from ipfml import utils
      >>> import numpy as np
      >>> y_values = np.array([5, 20, 4, 18, 19, 18, 7, 4])
      >>> area = utils.integral_area_simps(y_values, dx=5)
      >>> area
      460.0

`ipfml.utils.``integral_area_trapz`(y_values, dx)

  Returns area under curves from provided data points using Trapezium rule

  Parameters: | y_values – y values of curve dx – number of unit for x axis
  Returns: | Area under curves obtained from these points

  Example:

      >>> from ipfml import utils
      >>> import numpy as np
      >>> y_values = np.array([5, 20, 4, 18, 19, 18, 7, 4])
      >>> area = utils.integral_area_trapz(y_values, dx=5)
      >>> area
      452.5

`ipfml.utils.``normalize_2D_arr`(arr)

  Return array normalize from its min and max values

  Parameters: | arr – 2D Numpy array
  Returns: | Normalized 2D Numpy array

  Example:

      >>> from PIL import Image
      >>> from ipfml import utils, processing
      >>> img = Image.open('./images/test_img.png')
      >>> img_mscn = processing.rgb_to_mscn(img)
      >>> img_normalized = utils.normalize_2D_arr(img_mscn)
      >>> img_normalized.shape
      (200, 200)

`ipfml.utils.``normalize_arr`(arr)

  Normalize data of 1D array shape

  Parameters: | arr – array data of 1D shape
  Returns: | Normalized 1D array

  Example:

      >>> from ipfml import utils
      >>> import numpy as np
      >>> arr = np.arange(11)
      >>> arr_normalized = utils.normalize_arr(arr)
      >>> arr_normalized[1]
      0.1

`ipfml.utils.``normalize_arr_with_range`(arr, min, max)

  Normalize data of 1D array shape

  Parameters: | arr – array data of 1D shape
  Returns: | Normalized 1D Numpy array

  Example:

      >>> from ipfml import utils
      >>> import numpy as np
      >>> arr = np.arange(11)
      >>> arr_normalized = utils.normalize_arr_with_range(arr, 0, 20)
      >>> arr_normalized[1]
      0.05